Growth hormone induces bone morphogenetic proteins and bone-related proteins in the developing rat periodontium

The hypothesis that growth hormone (GH) up-regulates the expression of enzymes, matrix proteins, and differentiation markers involved in mineralization of tooth and bone matrices was tested by the treatment of Lewis dwarf rats with GH over 5 days, The molar teeth and associated alveolar bone were processed for immunohistochemical demonstration of bone morphogenetic proteins 2 and 4 (BMP-2 and -4), bone morphogenetic protein type IA receptor (BMPR-IA), bone alkaline phosphatase (ALP), osteocalcin (OC), osteopontin (OPN), bone sialoprotein (BSP), and E11 protein (E11), The cementoblasts, osteoblasts, and periodontal ligament (PDL) cells responded to GH by expressing BMP-2 and -4, BMPR-IA, ALP, OC, and OPN and increasing the numbers of these cells. No changes were found in patterns of expression of the late differentiation markers BSP and E11 in response to GH, Thus, GH evokes expression of bone markers of early differentiation in cementoblasts, PDL cells, and osteoblasts of the periodontium. We propose that the induction of BMP-2 and -4 and their receptor by GH compliments the role of GH-induced insulin-like growth factor 1 (IGF-1) in promoting bone and tooth root formation.

Long-term survivor mixture model with random effects: application to a multi-centre clinical trial of carcinoma

A mixture model incorporating long-term survivors has been adopted in the field of biostatistics where some individuals may never experience the failure event under study. The surviving fractions may be considered as cured. In most applications, the survival times are assumed to be independent. However, when the survival data are obtained from a multi-centre clinical trial, it is conceived that the environ mental conditions and facilities shared within clinic affects the proportion cured as well as the failure risk for the uncured individuals. It necessitates a long-term survivor mixture model with random effects. In this paper, the long-term survivor mixture model is extended for the analysis of multivariate failure time data using the generalized linear mixed model (GLMM) approach. The proposed model is applied to analyse a numerical data set from a multi-centre clinical trial of carcinoma as an illustration. Some simulation experiments are performed to assess the applicability of the model based on the average biases of the estimates formed. Copyright (C) 2001 John Wiley & Sons, Ltd.